Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Huang, Chih Shuan; Huang, Ya; Chan, Din Yuen; Yang, Jar‐Ferr

doi:10.1186/s13640-020-00525-3

Cited by 11 publications

(7 citation statements)

References 37 publications

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“…As shown in Table 3, the average mismatching rate of our algorithm is reduced by 8.57% compared to the fixed‐size window Census transform algorithm, and our algorithm also significantly outperforms Algorithm 1 [30], Algorithm 2 [31], Algorithm 3 [32], Algorithm 4 [33], Algorithm 5 [34], Algorithm 6 [35], Algorithm 7 [36]. The matching accuracy in weak‐textured and depth‐discontinuity regions has a considerable improvement compared with many other algorithms, and the algorithm proposed in this paper is of the significant utility.…”

Section: Experimental Results and Analysismentioning

confidence: 95%

“…In order to reflect the superiority of our algorithm more objectively, the algorithm in this paper is compared with Algorithm 1 [30] Algorithm 2 [31], Algorithm 3 [32], Algorithm 4 [33], Algorithm 5 [34], Algorithm 6 [35], Algorithm 7 [36], and the fixed‐size window Census transform algorithm. The mismatching rate in the three kinds of regions is shown in Table 3.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Huang et al. [33] first use the Sobel algorithm to divide the image into the edge and non‐edge regions, and then use the cross window to divide the non‐edge regions into smooth and non‐smooth regions, and finally adaptively choose the appropriate algorithm among AD algorithm, gradient information and Census transform for matching cost calculation. Although this method takes advantage of the three different algorithms and improves the matching accuracy, it cannot eliminate the disadvantages of fixed‐size matching window and single matching cost calculation method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The stereo matching algorithm based on an improved adaptive support window

Liu

2022

IET Image Processing

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In binocular stereo matching, there has been a problem of low matching accuracy and noise immunity in discontinuous regions and weak-textured regions. This paper proposes a stereo matching algorithm based on an improved adaptive support window. In the cost computation stage, first, according to the preset arm length and colour threshold, a crossbased arm is obtained, which centres on the pixel to be matched; then the adaptive regions of the vertical arm and the horizontal arm are constructed respectively, which have different shape and size. Finally, the union of the two adaptive regions is used as the final support window of Census transform. Performance evaluations on Middlebury stereo data sets demonstrate that the proposed algorithm outperforms other seven most challenging stereo matching algorithms. The mismatching rate of this algorithm is greatly reduced, and the anti-noise performance is also improved considerably. Because the construction of the adaptive region is based on strict criteria and comprehensive consideration, the algorithm proposed in the paper can improve the matching accuracy in the weak-textured regions and discontinuous disparity regions.

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Section: Experimental Results and Analysismentioning

confidence: 95%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The stereo matching algorithm based on an improved adaptive support window

Liu

2022

IET Image Processing

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“…Since the pixel wise characterization play a major factor, a wide variety of these representations are used by researchers varying from a simple rgb representation of pixels to the other descriptors like census transform, scale invariant feature transform. Segment based super pixel technique is proposed in [16]. After finding the edges and matching cost, adaptive support weight is used in cost aggregation.…”

Section: Introductionmentioning

confidence: 99%

A deep learning based stereo matching model for autonomous vehicle

Deepa

Kupparu²

2023

IJ-AI

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<p><span lang="EN-US">Autonomous vehicle is one the prominent area of research in computer vision. In today’s AI world, the concept of autonomous vehicles has become popular largely to avoid accidents due to negligence of driver. Perceiving the depth of the surrounding region accurately is a challenging task in autonomous vehicles. Sensors like light detection and ranging can be used for depth estimation but these sensors are expensive. Hence stereo matching is an alternate solution to estimate the depth. The main difficulties observed in stereo matching is to minimize mismatches in the ill-posed regions, like occluded, texture less and discontinuous regions. This paper presents an efficient deep stereo matching technique for estimating disparity map from stereo images in ill-posed regions. The images from Middlebury stereo data set are used to assess the efficacy of the model proposed. The experimental outcome dipicts that the proposed model generates reliable results in the occluded, texture less and discontinuous regions as compared to the existing techniques.</span></p>

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“…Conventional algorithms are grouped into local and global algorithms. In local approaches, disparity is computed by comparing small areas [6] [7]. The disparity calculation relies on intensity in a defined support area.…”

Section: Introductionmentioning

confidence: 99%

An Improved Depth Estimation using Stereo Matching and Disparity Refinement Based on Deep Learning

Udupa

2023

IJACSA

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Stereo matching techniques are a vital subject in computer vision. It focuses on finding accurate disparity maps that find its use in several applications namely reconstruction of a 3D scene, navigation of robot, augmented reality. It is a method of obtaining corresponding matching point in stereo images to get disparity map. With additional details, this disparity map could be converted into a depth of a scene. Obtaining an efficient disparity map in the texture less, occluded, and discontinuous areas is a difficult job. A matching cost using an improvised Census transform and an optimization framework is proposed to produce an initial disparity map. The classic Census transform focus on the value of pixel at the center. If this pixel is prone to noisy condition, then the census encoding may differ which leads to mismatches. To overcome this issue an improved census transform based on weighted sum values of the neighborhood pixels is proposed which suppresses the noise during stereo matching. Additionally, a deep learning based disparity refinement technique using the generative adversarial network to handle texture less, occluded, and discontinuous areas is proposed. The suggested method offers cutting-edge performance in terms of both qualitative and quantitative outcomes.

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Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

Cited by 11 publications

References 37 publications

The stereo matching algorithm based on an improved adaptive support window

The stereo matching algorithm based on an improved adaptive support window

A deep learning based stereo matching model for autonomous vehicle

An Improved Depth Estimation using Stereo Matching and Disparity Refinement Based on Deep Learning

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